1. Field of the Invention
This invention pertains to the general field of spatial and temporal pattern recognition. In particular, it provides a new and improved system for learning and recognizing information corresponding to sequences of input data, such as signals from audio or visual patterns.
2. Description of the Prior Art
Much effort has been devoted to the field of pattern recognition, especially in relation to voice and images. The development of reliable apparatus for this purpose is critical to the successful implementation of numerous robotic applications that have only been realized in rudimentary fashion to date. These include, for example, speech recognition and image recognition for direct conversion of spoken language into written text, and vice versa.
Among the various approaches followed to design effective information processors, researchers have often adopted the neural-system model for simulating the mechanisms of learning and recognizing information. See, for example, Hopfield, J. et al., "Computing with Neural Circuits: A Model," Science, Vol. 233, Aug. 8, 1986, pp. 625-633; Rumelhart, D. et al., "Parallel Distributed Processing: Explorations in the Microstructure of Cognition," Vol. 1, The MIT Press, Cambridge, Mass., 1986; Dehaene, S. et al., "Neural Networks that Learn Temporal Sequences by Selection," Proc. Natl. Acad. Sci. USA, Vol 84, May, 1987, Biophysics, pp. 2727-2731; Fukushima, K., "A Neural Network for Visual Pattern Recognition," Computer, March 1988, pp. 65-75; Carpenter, G. et al., "The Art of Adaptive Pattern Recognition by a Self-Organizing Neural Network," Computer, March 1988, pp. 77-88; Linsker, R., "Self-Organization in a Perceptual Network," Computer, March, 1988, pp. 105-117; and Lukashin, A., "A learned Neural Network that Simulates Properties of the Neuronal Population Vector," Biological Cybernetics, Vol. 63, 1990, pp. 377-382. These approaches strive to simulate the biological information processing behavior of the neural system and use mathematical models developed from vectors of relevant input data, usually through iterative adaptive techniques. These models are constructed for specific applications, rather than for a general simulation of the neural system behavior. U.S. Pat. No. 4,941,122 to Weideman (1990), for instance, describes a neural network system for processing images.
All of the methods found in the prior art utilize input data based on the absolute values of parameters that are characteristic of the information being processed. There is some evidence, though, that the biological brain processes relative values of sensory input rather than absolute values. In seeing, for example, the neural system does not process absolute levels of color and intensity; instead, the retinas of the eyes generate signals at each point within the visual field which are proportional to the value of color or intensity at that point relative to the color or intensity immediately surrounding that point. Therefore, there exists a need for a neural-system model that processes relative values of the input signals, rather than absolute values. This approach would seem to offer the degree of flexibility missing in the prior art for recognizing perceptual objects under a wide variety of environmental conditions. In addition, most existing neural-network approaches to learning patterns involve thousands of iterations, which take time and make real-time processing difficult. Therefore, it would be very desirable to have a system that is capable of learning in a single-pass process.